Remove old interface and deprecate the arguments

Author: JQGoh

nbs/common.base_model.ipynb

@@ -27,14 +27,24 @@
    "execution_count": null,
    "id": "1c7c2ba5-19ee-421e-9252-7224b03f5201",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "2024-12-11 17:06:11,409\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n",
+      "2024-12-11 17:06:11,467\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n"
+     ]
+    }
+   ],
    "source": [
     "#| export\n",
     "import inspect\n",
     "import random\n",
     "import warnings\n",
     "from contextlib import contextmanager\n",
-    "from copy import deepcopy\n",
     "from dataclasses import dataclass\n",
     "\n",
     "import fsspec\n",
@@ -121,10 +131,6 @@
     "        random_seed,\n",
     "        loss,\n",
     "        valid_loss,\n",
-    "        optimizer,\n",
-    "        optimizer_kwargs,\n",
-    "        lr_scheduler,\n",
-    "        lr_scheduler_kwargs,\n",
     "        futr_exog_list,\n",
     "        hist_exog_list,\n",
     "        stat_exog_list,\n",
@@ -150,18 +156,6 @@
     "        self.train_trajectories = []\n",
     "        self.valid_trajectories = []\n",
     "\n",
-    "        # Optimization\n",
-    "        if optimizer is not None and not issubclass(optimizer, torch.optim.Optimizer):\n",
-    "            raise TypeError(\"optimizer is not a valid subclass of torch.optim.Optimizer\")\n",
-    "        self.optimizer = optimizer\n",
-    "        self.optimizer_kwargs = optimizer_kwargs if optimizer_kwargs is not None else {}\n",
-    "\n",
-    "        # lr scheduler\n",
-    "        if lr_scheduler is not None and not issubclass(lr_scheduler, torch.optim.lr_scheduler.LRScheduler):\n",
-    "            raise TypeError(\"lr_scheduler is not a valid subclass of torch.optim.lr_scheduler.LRScheduler\")\n",
-    "        self.lr_scheduler = lr_scheduler\n",
-    "        self.lr_scheduler_kwargs = lr_scheduler_kwargs if lr_scheduler_kwargs is not None else {}\n",
-    "\n",
     "        # customized by set_configure_optimizers()\n",
     "        self.config_optimizers = None\n",
     "\n",
@@ -412,41 +406,19 @@
     "\n",
     "    def configure_optimizers(self):\n",
     "        if self.config_optimizers is not None:\n",
+    "            # return the customized optimizer settings if specified\n",
     "            return self.config_optimizers\n",
-    "                \n",
-    "        if self.optimizer:\n",
-    "            optimizer_signature = inspect.signature(self.optimizer)\n",
-    "            optimizer_kwargs = deepcopy(self.optimizer_kwargs)\n",
-    "            if 'lr' in optimizer_signature.parameters:\n",
-    "                if 'lr' in optimizer_kwargs:\n",
-    "                    warnings.warn(\"ignoring learning rate passed in optimizer_kwargs, using the model's learning rate\")\n",
-    "                optimizer_kwargs['lr'] = self.learning_rate\n",
-    "            optimizer = self.optimizer(params=self.parameters(), **optimizer_kwargs)\n",
-    "        else:\n",
-    "            if self.optimizer_kwargs:\n",
-    "                warnings.warn(\n",
-    "                    \"ignoring optimizer_kwargs as the optimizer is not specified\"\n",
-    "                )\n",
-    "            optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
     "        \n",
-    "        lr_scheduler = {'frequency': 1, 'interval': 'step'}\n",
-    "        if self.lr_scheduler:\n",
-    "            lr_scheduler_signature = inspect.signature(self.lr_scheduler)\n",
-    "            lr_scheduler_kwargs = deepcopy(self.lr_scheduler_kwargs)\n",
-    "            if 'optimizer' in lr_scheduler_signature.parameters:\n",
-    "                if 'optimizer' in lr_scheduler_kwargs:\n",
-    "                    warnings.warn(\"ignoring optimizer passed in lr_scheduler_kwargs, using the model's optimizer\")\n",
-    "                    del lr_scheduler_kwargs['optimizer']\n",
-    "            lr_scheduler['scheduler'] = self.lr_scheduler(optimizer=optimizer, **lr_scheduler_kwargs)\n",
-    "        else:\n",
-    "            if self.lr_scheduler_kwargs:\n",
-    "                warnings.warn(\n",
-    "                    \"ignoring lr_scheduler_kwargs as the lr_scheduler is not specified\"\n",
-    "                )            \n",
-    "            lr_scheduler['scheduler'] = torch.optim.lr_scheduler.StepLR(\n",
+    "        # default choice\n",
+    "        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
+    "        scheduler = {\n",
+    "            \"scheduler\": torch.optim.lr_scheduler.StepLR(\n",
     "                optimizer=optimizer, step_size=self.lr_decay_steps, gamma=0.5\n",
-    "            )\n",
-    "        return {'optimizer': optimizer, 'lr_scheduler': lr_scheduler}\n",
+    "            ),\n",
+    "            \"frequency\": 1,\n",
+    "            \"interval\": \"step\",\n",
+    "        }\n",
+    "        return {\"optimizer\": optimizer, \"lr_scheduler\": scheduler}\n",
     "\n",
     "    def set_configure_optimizers(\n",
     "            self, \n",
@@ -528,6 +500,22 @@
     "            model.load_state_dict(content[\"state_dict\"], strict=True)\n",
     "        return model"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "077ea025",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b36e87a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

nbs/common.base_multivariate.ipynb

@@ -105,20 +105,12 @@
     "                 drop_last_loader=False,\n",
     "                 random_seed=1, \n",
     "                 alias=None,\n",
-    "                 optimizer=None,\n",
-    "                 optimizer_kwargs=None,\n",
-    "                 lr_scheduler=None,\n",
-    "                 lr_scheduler_kwargs=None,\n",
     "                 dataloader_kwargs=None,\n",
     "                 **trainer_kwargs):\n",
     "        super().__init__(\n",
     "            random_seed=random_seed,\n",
     "            loss=loss,\n",
-    "            valid_loss=valid_loss,\n",
-    "            optimizer=optimizer,\n",
-    "            optimizer_kwargs=optimizer_kwargs,\n",
-    "            lr_scheduler=lr_scheduler,\n",
-    "            lr_scheduler_kwargs=lr_scheduler_kwargs,            \n",
+    "            valid_loss=valid_loss,        \n",
     "            futr_exog_list=futr_exog_list,\n",
     "            hist_exog_list=hist_exog_list,\n",
     "            stat_exog_list=stat_exog_list,\n",

nbs/common.base_recurrent.ipynb

@@ -111,20 +111,12 @@
     "                 drop_last_loader=False,\n",
     "                 random_seed=1, \n",
     "                 alias=None,\n",
-    "                 optimizer=None,\n",
-    "                 optimizer_kwargs=None,\n",
-    "                 lr_scheduler=None,\n",
-    "                 lr_scheduler_kwargs=None,\n",
     "                 dataloader_kwargs=None,\n",
     "                 **trainer_kwargs):\n",
     "        super().__init__(\n",
     "            random_seed=random_seed,\n",
     "            loss=loss,\n",
     "            valid_loss=valid_loss,\n",
-    "            optimizer=optimizer,\n",
-    "            optimizer_kwargs=optimizer_kwargs,\n",
-    "            lr_scheduler=lr_scheduler,\n",
-    "            lr_scheduler_kwargs=lr_scheduler_kwargs,\n",
     "            futr_exog_list=futr_exog_list,\n",
     "            hist_exog_list=hist_exog_list,\n",
     "            stat_exog_list=stat_exog_list,\n",

nbs/common.base_windows.ipynb

@@ -115,20 +115,12 @@
     "                 drop_last_loader=False,\n",
     "                 random_seed=1,\n",
     "                 alias=None,\n",
-    "                 optimizer=None,\n",
-    "                 optimizer_kwargs=None,\n",
-    "                 lr_scheduler=None,\n",
-    "                 lr_scheduler_kwargs=None,\n",
     "                 dataloader_kwargs=None,\n",
     "                 **trainer_kwargs):\n",
     "        super().__init__(\n",
     "            random_seed=random_seed,\n",
     "            loss=loss,\n",
     "            valid_loss=valid_loss,\n",
-    "            optimizer=optimizer,\n",
-    "            optimizer_kwargs=optimizer_kwargs,\n",
-    "            lr_scheduler=lr_scheduler,\n",
-    "            lr_scheduler_kwargs=lr_scheduler_kwargs,\n",
     "            futr_exog_list=futr_exog_list,\n",
     "            hist_exog_list=hist_exog_list,\n",
     "            stat_exog_list=stat_exog_list,\n",